An Introduction to Image Segmentation andObject-oriented Analysis

Wayne Walker and Ned Horning

University Mulawarman, Samarinda, Indonesia November 8-12, 2010

Images are made up of objects and not pixels!!

• Process of grouping pixels

• Intent is usually to simplify the image into meaningful pixel groupings (i.e., segments/objects)

• Segments are relatively homogeneous with regard to one or more characteristics

What is Segmentation?

• Bottom up• Image simplification• Image classification• Image compression• Edge detection• Object-Based Image

Analysis (OBIA)

• Top down• Feature extraction• Object recognition

Uses of Segmentation

• Classification is performed on objects instead of pixels (increased signal-to-noise ratio)

• Classification is performed using meaningful objects

• Algorithms operate on many more object-related features than typically available with pixel-based approaches

• Reduces salt and pepper effect of classifications

• Speeds up processing

Why Segment Images before Classification?

• Features in an image can vary from fine to coarse scale

• Need to find a balance (compromise) between too many and too few segments

• Multi-scale approach identifies features at appropriate scales Images from: http://fuentek.net/technologies/rhseg.htm

Segmentation and Scale

Region growing• Find similar pixels from

a seed and neighboring pixels

Watershed detection• Mostly for gray-scale

images• Treats image like a

topographic surfaceMean shift

• Used for segmentation and filtering

• Uses feature space and spatial domain

From: Mean shift: A robust approach toward feature space analysis

Algorithms for Segmenting Remotely Sensed Images

Spectral • Mean• Variance• Range• Ratios

Spatial• Area• Shape• Location• Context / Neighborhoods

Information Derived from Segments

• eCognition

• Spring

• RHSEG

• OTB/Monteverdi

Software for Image Segmentation

• Most popular segmentation software

• A stand-alone product for object-based image analysis

• Uses region growing

• eCognition now owned by Trimble

• www.ecognition.com

eCognition

eCognition Background: Software Features

• Multi-scale (hierarchical)

• Multi-source

• Multi-resolution

• Multi-temporal

eCognition: Multi-scale/Multi-source Segmentation

• Freeware GIS / Image processing

• Region growing and watershed segmentation

• http://www.dpi.inpe.br/spring/english/

Spring

• Open source software from NASA

• Stand-alone package for unsupervised image classification using sub and super-sets of segments

• User labels each segmentation level

RHSEG

• Open source software

• OTB is a software library and Monteverdi is the application

• Integrating with QGIS

• Watershed, region growing, level sets, and mean shift segmentation

OTM/Monteverdi

Segmentation software evaluations• http://www.ioer.de/segmentation-evaluation/

Berkeley segmentation dataset and benchmark• http://www.eecs.berkeley.edu/Research/Projects/CS/vis

ion/bsds/

Segmentation Resources

Thank you!